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@ -2331,6 +2331,141 @@ IL13 and IL15 were found predictive in combination with these using SR
 \section{discussion}
 % optimization of process features
 % TODO this sounds like total fluff
 CPPs modeling and understanding are critical to new product development and in
 cell therapy development, it can have life-saving implications. The challenges
 for effective modeling grow with the increasing complexity of processes due to
 high dimensionality, and the potential for process interactions and nonlinear
 relationships. Another critical challenge is the limited amount of available
 data, mostly small DOE datasets. SR has the necessary capabilities to resolve
 the issues of process effects modeling and has been applied across multiple
 industries12. SR discovers mathematical expressions that fit a given sample and
 differs from conventional regression techniques in that a model structure is not
 defined a priori13. Hence, a key advantage of this methodology is that
 transparent, human-interpretable models can be generated from small and large
 datasets with no prior assumptions14,15.
 Since the model search process lets the data determine the model, diverse and
 competitive (e.g., accuracy, complexity) model structures are typically
 discovered. An ensemble of diverse models can be formed where its constituent
 models will tend to agree when constrained by observed data yet diverge in new
 regions. Collecting data in these regions helps to ensure that the target system
 is accurately modeled, and its optimum is accurately located14,15. Exploiting
 these features allows adaptive data collection and interactive modeling.
 Consequently, this adaptive-DOE approach is useful in a variety of scenarios,
 including maximizing model validity for model-based decision making, optimizing
 processing parameters to maximize target yields, and developing emulators for
 online optimization and human understanding14,15.
 % predictive features
 An in-depth characterization of potential DMS-based T-cell CQAs includes a list
 of cytokine and NMR features from media samples that are crucial in many aspects
 of T cell fate decisions and effector functions of immune cells. Cytokine
 features were observed to slightly improve prediction and dominated the ranking
 of important features and variable combinations when modeling together with NMR
 media analysis and process parameters (Fig.3b,d).
 Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and IL15 were
 biologically assessed in terms of their known functions and activities
 associated with T cells. T helper cells secrete more cytokines than T cytotoxic
 cells, as per their main functions, and activated T cells secrete more cytokines
 than resting T cells. It is possible that some cytokines simply reflect the
 CD4+/CD8+ ratio and the activation degree by proxy proliferation. However, the
 exact ratio of expected cytokine abundance is less clear and depends on the
 subtypes present, and thus examination of each relevant cytokine is needed.
 IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
 dampen its effect on T cells16. Since IL2R was much greater than IL2 in
 solution, this might reduce the overall effect of IL2, which could be further
 investigated by blocking IL2R with an antibody. In T cells, TNF can increase
 IL2R, proliferation, and cytokine production18. It may also induce apoptosis
 depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF
 has both a soluble and membrane-bound form, this may either increase or decrease
 CD4+ ratio and/or memory T cells depending on the ratio of the membrane to
 soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to
 understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13
 is known to be critical for Th2 response and therefore could be secreted if
 there are significant Th2 T cells already present in the starting population19.
 This cytokine has limited signaling in T cells and is thought to be more of an
 effector than a differentiation cytokine20. It might be emerging as relevant due
 to an initially large number of Th2 cells or because Th2 cells were
 preferentially expanded; indeed, IL4, also found important, is the conical
 cytokine that induces Th2 cell differentiation (Fig.3). The role of these
 cytokines could be investigated by quantifying the Th1/2/17 subsets both in the
 starting population and longitudinally. Similar to IL13, IL17 is an effector
 cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of
 T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells,
 but it is not clear if this cytokine is inducing differential expansion of CD8+
 T cells or if it is simply a covariate with another cytokine inducing this
 expansion22. Finally, IL15 has been shown to be essential for memory signaling
 and effective in skewing CAR-T cells toward the Tscm phenotype when using
 membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its
 ability to induce large numbers of memory T cells by functioning in an
 autocrine/paracrine manner and could be explored by blocking either the cytokine
 or its receptor.
 % FIGURE correlation plots from supplement (as alluded to here)
 Moreover, many predictive metabolites found here are consistent with metabolic
 activity associated with T cell activation and differentiation, yet it is not
 clear how the various combinations of metabolites relate with each other in a
 heterogeneous cell population. Formate and lactate were found to be highly
 predictive and observed to positively correlate with higher values of total live
 CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the
 one-carbon cycle implicated in promoting T cell activation24. Importantly, this
 cycle occurs between the cytosol and mitochondria of cells and formate
 excreted25. Mitochondrial biogenesis and function are shown necessary for memory
 cell persistence26,27. Therefore, increased formate in media could be an
 indicator of one-carbon metabolism and mitochondrial activity in the culture.
 In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate
 is the end-product of aerobic glycolysis, characteristic of highly proliferating
 cells and activated T cells28,29. Glucose import and glycolytic genes are
 immediately upregulated in response to T cell stimulation, and thus generation
 of lactate. At earlier time-points, this abundance suggests a more robust
 induction of glycolysis and higher overall T cell proliferation. Interestingly,
 our models indicate that higher lactate predicts higher CD4+, both in total and
 in proportion to CD8+, seemingly contrary to previous studies showing that CD8+
 T cells rely more on glycolysis for proliferation following activation30. It may
 be that glycolytic cells dominate in the culture at the early time points used
 for prediction, and higher lactate reflects more cells.
 % TODO not sure how much I should include here since I didn't do this analysis
 % AT ALL
 % Ethanol patterns are difficult to interpret since its production in mammalian
 % cells is still poorly understood31. Fresh media analysis indicates ethanol
 % presence in the media used, possibly utilized as a carrier solvent for certain
 % formula components. However, this does not explain the high variability and
 % trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
 % chemical, variation could be introduced by sample handling throughout the
 % analysis process. Nonetheless, it is also possible that ethanol excreted into
 % media over time, impacting processes regulating redox and reactive oxygen
 % species which have previously been shown to be crucial in T cell signaling and
 % differentiation32.
 % this looks fine since it is just parroting sources, just need to paraphrase a
 % little
 Metabolites that consistently decreased over time are consistent with the
 primary carbon source (glucose) and essential amino acids (BCAA, histidine) that
 must be continually consumed by proliferating cells. Moreover, the inclusion of
 glutamine in our predictive models also suggests the importance of other carbon
 sources for certain T cell subpopulations. Glutamine can be used for oxidative
 energy metabolism in T cells without the need for glycolysis30. Overall, these
 results are consistent with existing literature that show different T cell
 subtypes require different relative levels of glycolytic and oxidative energy
 metabolism to sustain the biosynthetic and signaling needs of their respective
 phenotypes33,34. It is worth noting that the trends of metabolite abundance here
 are potentially confounded by the partial replacement of media that occurred
 periodically during expansion (Methods), thus likely diluting some metabolic
 byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e.
 glucose, amino acids). More definitive conclusions of metabolic activity across
 the expanding cell population can be addressed by a closed system, ideally with
 on-line process sensors and controls for formate, lactate, along with ethanol
 and glucose.
 \chapter{aim 2b}\label{aim2b}
 \section{introduction}